Overview

Dataset statistics

Number of variables27
Number of observations266093
Missing cells582597
Missing cells (%)8.1%
Duplicate rows2400
Duplicate rows (%)0.9%
Total size in memory54.8 MiB
Average record size in memory216.0 B

Variable types

CAT12
NUM10
DATE4
BOOL1

Warnings

MANDT has constant value "266093" Constant
MWST2 has constant value "266093" Constant
Dataset has 2400 (0.9%) duplicate rows Duplicates
KNA1_ORT01 has a high cardinality: 3311 distinct values High cardinality
T003T_LTEXT is highly correlated with BLARTHigh correlation
BLART is highly correlated with T003T_LTEXTHigh correlation
MANSP has 258474 (97.1%) missing values Missing
CTLPC has 112180 (42.2%) missing values Missing
HISTORICRATING has 105971 (39.8%) missing values Missing
CURRENTRATING has 105972 (39.8%) missing values Missing
VALUE_EUR is highly skewed (γ1 = 82.8805719) Skewed
MWSTS is highly skewed (γ1 = 61.68251871) Skewed
BUKRS has 40009 (15.0%) zeros Zeros
VALUE_EUR has 5268 (2.0%) zeros Zeros
MWSTS has 260868 (98.0%) zeros Zeros

Reproduction

Analysis started2021-03-22 18:24:28.228395
Analysis finished2021-03-22 18:25:25.563956
Duration57.34 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

MANDT
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
310
266093 
ValueCountFrequency (%) 
310266093100.0%
 
2021-03-22T18:25:25.641581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-22T18:25:25.709352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:25.773181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

BUKRS
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.049046762
Minimum0
Maximum5
Zeros40009
Zeros (%)15.0%
Memory size2.0 MiB
2021-03-22T18:25:25.870921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.841818731
Coefficient of variation (CV)0.6040637862
Kurtosis-1.219418673
Mean3.049046762
Median Absolute Deviation (MAD)2
Skewness-0.4498366675
Sum811330
Variance3.392296239
MonotocityNot monotonic
2021-03-22T18:25:25.969693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
59162334.4%
 
36652625.0%
 
04000915.0%
 
13626513.6%
 
42701610.2%
 
246541.7%
 
ValueCountFrequency (%) 
04000915.0%
 
13626513.6%
 
246541.7%
 
36652625.0%
 
42701610.2%
 
ValueCountFrequency (%) 
59162334.4%
 
42701610.2%
 
36652625.0%
 
246541.7%
 
13626513.6%
 

GJAHR
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
2018
173519 
2019
92574 
ValueCountFrequency (%) 
201817351965.2%
 
20199257434.8%
 
2021-03-22T18:25:26.092331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-22T18:25:26.174142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:26.250904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

HKONT
Real number (ℝ≥0)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.939735356
Minimum0
Maximum13
Zeros17
Zeros (%)< 0.1%
Memory size2.0 MiB
2021-03-22T18:25:26.350281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median5
Q35
95-th percentile11
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.712329671
Coefficient of variation (CV)0.5490840046
Kurtosis2.10047292
Mean4.939735356
Median Absolute Deviation (MAD)0
Skewness1.063818812
Sum1314429
Variance7.356732244
MonotocityNot monotonic
2021-03-22T18:25:26.472795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%) 
518661770.1%
 
14744117.8%
 
11130424.9%
 
1399403.7%
 
1024880.9%
 
719240.7%
 
418850.7%
 
613410.5%
 
310070.4%
 
123080.1%
 
Other values (4)100< 0.1%
 
ValueCountFrequency (%) 
017< 0.1%
 
14744117.8%
 
225< 0.1%
 
310070.4%
 
418850.7%
 
ValueCountFrequency (%) 
1399403.7%
 
123080.1%
 
11130424.9%
 
1024880.9%
 
956< 0.1%
 

KUNNR
Real number (ℝ≥0)

Distinct23067
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9589.260105
Minimum0
Maximum23823
Zeros2
Zeros (%)< 0.1%
Memory size2.0 MiB
2021-03-22T18:25:26.628379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile788
Q14008
median9103
Q315359
95-th percentile20530
Maximum23823
Range23823
Interquartile range (IQR)11351

Descriptive statistics

Standard deviation6401.648935
Coefficient of variation (CV)0.6675852845
Kurtosis-1.000644112
Mean9589.260105
Median Absolute Deviation (MAD)5500
Skewness0.3799429382
Sum2551634989
Variance40981109.09
MonotocityNot monotonic
2021-03-22T18:25:26.773539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1781226541.0%
 
206024500.9%
 
198120930.8%
 
518618730.7%
 
198917980.7%
 
920017700.7%
 
637615910.6%
 
2033315840.6%
 
919714380.5%
 
272813800.5%
 
Other values (23057)24746293.0%
 
ValueCountFrequency (%) 
02< 0.1%
 
12< 0.1%
 
22< 0.1%
 
32< 0.1%
 
424< 0.1%
 
ValueCountFrequency (%) 
238231< 0.1%
 
2382265< 0.1%
 
2382118< 0.1%
 
2382021< 0.1%
 
238197< 0.1%
 

PRCTR
Real number (ℝ≥0)

Distinct226
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.10296776
Minimum0
Maximum229
Zeros63
Zeros (%)< 0.1%
Memory size2.0 MiB
2021-03-22T18:25:26.929715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q130
median80
Q3142
95-th percentile220
Maximum229
Range229
Interquartile range (IQR)112

Descriptive statistics

Standard deviation75.24369804
Coefficient of variation (CV)0.8259192855
Kurtosis-1.066248709
Mean91.10296776
Median Absolute Deviation (MAD)62
Skewness0.4801276873
Sum24241862
Variance5661.614095
MonotocityNot monotonic
2021-03-22T18:25:27.073967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1423494313.1%
 
1224798.4%
 
7183696.9%
 
220114784.3%
 
30106144.0%
 
219103683.9%
 
484893.2%
 
3474752.8%
 
4568062.6%
 
22959032.2%
 
Other values (216)12916948.5%
 
ValueCountFrequency (%) 
063< 0.1%
 
1224798.4%
 
21< 0.1%
 
31< 0.1%
 
484893.2%
 
ValueCountFrequency (%) 
22959032.2%
 
22826< 0.1%
 
22724< 0.1%
 
2264230.2%
 
2255320.2%
 

KNA1_LAND1
Categorical

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
SE
90751 
NL
66353 
DK
39385 
GB
34317 
NO
27098 
Other values (45)
 
8189
ValueCountFrequency (%) 
SE9075134.1%
 
NL6635324.9%
 
DK3938514.8%
 
GB3431712.9%
 
NO2709810.2%
 
IE63202.4%
 
FI5050.2%
 
FO2140.1%
 
BE1600.1%
 
GL1450.1%
 
Other values (40)8450.3%
 
2021-03-22T18:25:27.230314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)< 0.1%
2021-03-22T18:25:27.347744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

KNA1_ORT01
Categorical

HIGH CARDINALITY

Distinct3311
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
OSLO
 
9459
Stockholm
 
6216
ROTTERDAM
 
4933
Uppsala
 
3849
Göteborg
 
3234
Other values (3306)
238402 
ValueCountFrequency (%) 
OSLO94593.6%
 
Stockholm62162.3%
 
ROTTERDAM49331.9%
 
Uppsala38491.4%
 
Göteborg32341.2%
 
Jönköping32101.2%
 
Hallsberg31251.2%
 
AMSTERDAM27521.0%
 
Eastleigh26901.0%
 
Altrincham26551.0%
 
Other values (3301)22397084.2%
 
2021-03-22T18:25:27.483381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique303 ?
Unique (%)0.1%
2021-03-22T18:25:27.614031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length26
Median length8
Mean length7.997876682
Min length2

ZTERM
Real number (ℝ)

Distinct150
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.72431443
Minimum-1
Maximum154
Zeros91
Zeros (%)< 0.1%
Memory size2.0 MiB
2021-03-22T18:25:27.733749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile29
Q129
median33
Q389
95-th percentile116
Maximum154
Range155
Interquartile range (IQR)60

Descriptive statistics

Standard deviation32.09569818
Coefficient of variation (CV)0.5759729574
Kurtosis-1.145431392
Mean55.72431443
Median Absolute Deviation (MAD)8
Skewness0.6085237167
Sum14827850
Variance1030.133842
MonotocityNot monotonic
2021-03-22T18:25:27.859374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2911165642.0%
 
613083511.6%
 
101188087.1%
 
97145845.5%
 
33145255.5%
 
116128864.8%
 
6979533.0%
 
8972452.7%
 
10067062.5%
 
3541081.5%
 
Other values (140)3678713.8%
 
ValueCountFrequency (%) 
-1113< 0.1%
 
091< 0.1%
 
15< 0.1%
 
22420.1%
 
36< 0.1%
 
ValueCountFrequency (%) 
1541< 0.1%
 
1531< 0.1%
 
1522< 0.1%
 
1512< 0.1%
 
1502< 0.1%
 

DUE_DATE_SOURCE
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
ZBD1T
222594 
ZFBDT
30767 
ZBD2T
 
12490
ZBD3T
 
242
ValueCountFrequency (%) 
ZBD1T22259483.7%
 
ZFBDT3076711.6%
 
ZBD2T124904.7%
 
ZBD3T2420.1%
 
2021-03-22T18:25:27.981048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-22T18:25:28.040888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:28.115727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length5
Min length5

VALUE_EUR
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct4659
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193.2596611
Minimum0
Maximum301241
Zeros5268
Zeros (%)2.0%
Memory size2.0 MiB
2021-03-22T18:25:28.221442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median33
Q3100
95-th percentile652
Maximum301241
Range301241
Interquartile range (IQR)90

Descriptive statistics

Standard deviation1473.405854
Coefficient of variation (CV)7.623969978
Kurtosis12607.00684
Mean193.2596611
Median Absolute Deviation (MAD)28
Skewness82.8805719
Sum51425043
Variance2170924.811
MonotocityNot monotonic
2021-03-22T18:25:28.337741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
191453.4%
 
280523.0%
 
374772.8%
 
466752.5%
 
562632.4%
 
656922.1%
 
755392.1%
 
052682.0%
 
850871.9%
 
948081.8%
 
Other values (4649)20208775.9%
 
ValueCountFrequency (%) 
052682.0%
 
191453.4%
 
280523.0%
 
374772.8%
 
466752.5%
 
ValueCountFrequency (%) 
3012411< 0.1%
 
2675131< 0.1%
 
1618211< 0.1%
 
1588101< 0.1%
 
1300761< 0.1%
 

MWSTS
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct2401
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean552.4894037
Minimum0
Maximum1750000
Zeros260868
Zeros (%)98.0%
Memory size2.0 MiB
2021-03-22T18:25:28.482589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1750000
Range1750000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12380.04842
Coefficient of variation (CV)22.40775721
Kurtosis5169.13418
Mean552.4894037
Median Absolute Deviation (MAD)0
Skewness61.68251871
Sum147013562.9
Variance153265598.9
MonotocityNot monotonic
2021-03-22T18:25:28.614578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
026086898.0%
 
25000128< 0.1%
 
12500105< 0.1%
 
3750089< 0.1%
 
5000062< 0.1%
 
1875057< 0.1%
 
1000047< 0.1%
 
1500045< 0.1%
 
1125043< 0.1%
 
625038< 0.1%
 
Other values (2391)46111.7%
 
ValueCountFrequency (%) 
026086898.0%
 
12< 0.1%
 
1.052< 0.1%
 
1.892< 0.1%
 
2.312< 0.1%
 
ValueCountFrequency (%) 
17500001< 0.1%
 
1340014.51< 0.1%
 
1197549.751< 0.1%
 
1156103.751< 0.1%
 
1104242.751< 0.1%
 

MWST2
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
266093 
ValueCountFrequency (%) 
0266093100.0%
 
2021-03-22T18:25:28.708289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

BLART
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
RV
261743 
ZF
 
2512
ZA
 
1838
ValueCountFrequency (%) 
RV26174398.4%
 
ZF25120.9%
 
ZA18380.7%
 
2021-03-22T18:25:28.786375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-22T18:25:28.864514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:28.942624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

T003T_LTEXT
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
Billing doc.transfer
261743 
Partial Bill. SD
 
2512
Down paym. req. SD
 
1838
ValueCountFrequency (%) 
Billing doc.transfer26174398.4%
 
Partial Bill. SD25120.9%
 
Down paym. req. SD18380.7%
 
2021-03-22T18:25:29.036352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-22T18:25:29.098839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:29.176946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length20
Mean length19.94842405
Min length16

TBSLT_LTEXT
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
Invoice
247875 
Credit memo
 
10560
Payment difference
 
5700
Down payment request
 
1838
advances Third Party
 
64
Other values (3)
 
56
ValueCountFrequency (%) 
Invoice24787593.2%
 
Credit memo105604.0%
 
Payment difference57002.1%
 
Down payment request18380.7%
 
advances Third Party64< 0.1%
 
Reverse invoice44< 0.1%
 
Incoming payment6< 0.1%
 
Reverse credit memo6< 0.1%
 
2021-03-22T18:25:29.282885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-22T18:25:29.365307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:29.717344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length7
Mean length7.48909216
Min length7

MANSP
Categorical

MISSING

Distinct11
Distinct (%)0.1%
Missing258474
Missing (%)97.1%
Memory size2.0 MiB
H
4788 
A
1067 
2
610 
7
 
470
1
 
466
Other values (6)
 
218
ValueCountFrequency (%) 
H47881.8%
 
A10670.4%
 
26100.2%
 
74700.2%
 
14660.2%
 
G90< 0.1%
 
660< 0.1%
 
828< 0.1%
 
323< 0.1%
 
511< 0.1%
 
(Missing)25847497.1%
 
2021-03-22T18:25:29.828302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-22T18:25:29.932829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.942734307
Min length1

CTLPC
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)< 0.1%
Missing112180
Missing (%)42.2%
Infinite0
Infinite (%)0.0%
Mean2.362191628
Minimum0
Maximum10
Zeros957
Zeros (%)0.4%
Memory size2.0 MiB
2021-03-22T18:25:30.029605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median2
Q33
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6722468341
Coefficient of variation (CV)0.2845860709
Kurtosis24.00671249
Mean2.362191628
Median Absolute Deviation (MAD)0
Skewness2.753259507
Sum363572
Variance0.4519158059
MonotocityNot monotonic
2021-03-22T18:25:30.119331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
210420539.2%
 
34126815.5%
 
471202.7%
 
09570.4%
 
101660.1%
 
983< 0.1%
 
148< 0.1%
 
535< 0.1%
 
831< 0.1%
 
(Missing)11218042.2%
 
ValueCountFrequency (%) 
09570.4%
 
148< 0.1%
 
210420539.2%
 
34126815.5%
 
471202.7%
 
ValueCountFrequency (%) 
101660.1%
 
983< 0.1%
 
831< 0.1%
 
535< 0.1%
 
471202.7%
 

HISTORICRATING
Categorical

MISSING

Distinct20
Distinct (%)< 0.1%
Missing105971
Missing (%)39.8%
Memory size2.0 MiB
R5+
39647 
R5-
30240 
R7+
24760 
R3+
18878 
R4-
13140 
Other values (15)
33457 
ValueCountFrequency (%) 
R5+3964714.9%
 
R5-3024011.4%
 
R7+247609.3%
 
R3+188787.1%
 
R4-131404.9%
 
R6-111074.2%
 
R6+101983.8%
 
R4+48071.8%
 
R221230.8%
 
R3-14940.6%
 
Other values (10)37281.4%
 
(Missing)10597139.8%
 
2021-03-22T18:25:30.230968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-22T18:25:30.339296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.986023683
Min length2

CURRENTRATING
Categorical

MISSING

Distinct20
Distinct (%)< 0.1%
Missing105972
Missing (%)39.8%
Memory size2.0 MiB
R5+
37930 
R5-
33013 
R7+
26075 
R3+
20166 
R6-
11178 
Other values (15)
31759 
ValueCountFrequency (%) 
R5+3793014.3%
 
R5-3301312.4%
 
R7+260759.8%
 
R3+201667.6%
 
R6-111784.2%
 
R4-106964.0%
 
R6+78342.9%
 
R4+51131.9%
 
R222000.8%
 
R3-14120.5%
 
Other values (10)45041.7%
 
(Missing)10597239.8%
 
2021-03-22T18:25:30.451224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-03-22T18:25:30.584169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.985339712
Min length2

DATUM
Date

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
Minimum2017-04-03 00:00:00
Maximum2019-07-01 00:00:00
2021-03-22T18:25:30.746557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:30.891366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
Distinct576
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
Minimum2017-05-23 00:00:00
Maximum2019-04-29 00:00:00
2021-03-22T18:25:31.032132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:31.181389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

GJAHR2
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.503279
Minimum2015
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size2.0 MiB
2021-03-22T18:25:31.314911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12016
median2018
Q32019
95-th percentile2020
Maximum2020
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.710152484
Coefficient of variation (CV)0.0008476578462
Kurtosis-1.270229236
Mean2017.503279
Median Absolute Deviation (MAD)2
Skewness-0.004062068041
Sum536843500
Variance2.924621519
MonotocityNot monotonic
2021-03-22T18:25:31.418294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
20204463416.8%
 
20154457616.8%
 
20184447916.7%
 
20194430316.6%
 
20174418516.6%
 
20164391616.5%
 
ValueCountFrequency (%) 
20154457616.8%
 
20164391616.5%
 
20174418516.6%
 
20184447916.7%
 
20194430316.6%
 
ValueCountFrequency (%) 
20204463416.8%
 
20194430316.6%
 
20184447916.7%
 
20174418516.6%
 
20164391616.5%
 
Distinct576
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
Minimum2017-05-23 00:00:00
Maximum2019-04-29 00:00:00
2021-03-22T18:25:31.529469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:31.665295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct567
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
Minimum2017-10-02 00:00:00
Maximum2019-09-24 00:00:00
2021-03-22T18:25:31.813248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:31.955667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
182366 
1
50260 
2
33467 
ValueCountFrequency (%) 
018236668.5%
 
15026018.9%
 
23346712.6%
 
2021-03-22T18:25:32.093262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-03-22T18:25:32.166106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:32.243861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

TIME_DELTA
Real number (ℝ)

Distinct341
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.86628735
Minimum-380
Maximum573
Zeros2574
Zeros (%)1.0%
Memory size2.0 MiB
2021-03-22T18:25:32.352747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-380
5-th percentile-9
Q12
median13
Q325
95-th percentile44
Maximum573
Range953
Interquartile range (IQR)23

Descriptive statistics

Standard deviation18.85942577
Coefficient of variation (CV)1.360091948
Kurtosis15.82541056
Mean13.86628735
Median Absolute Deviation (MAD)11
Skewness-0.2580093775
Sum3689722
Variance355.6779404
MonotocityNot monotonic
2021-03-22T18:25:32.493338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-1111064.2%
 
-2108684.1%
 
2790993.4%
 
1685053.2%
 
2983733.1%
 
782173.1%
 
681563.1%
 
2880873.0%
 
-376932.9%
 
976862.9%
 
Other values (331)17830367.0%
 
ValueCountFrequency (%) 
-3802< 0.1%
 
-2941< 0.1%
 
-2801< 0.1%
 
-2701< 0.1%
 
-2691< 0.1%
 
ValueCountFrequency (%) 
5731< 0.1%
 
3311< 0.1%
 
3242< 0.1%
 
3131< 0.1%
 
2822< 0.1%
 

Interactions

2021-03-22T18:25:06.035313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:06.197569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:06.345175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:06.493777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:06.643413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:06.787042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:06.926657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:07.075263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:07.217874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:07.359500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:07.495123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:07.633766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:07.771392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:07.909029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:08.057631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:08.202241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:08.341321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:08.489925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:08.632544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:08.777117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:08.914749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:09.053378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:09.198989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:09.352771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:09.541320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:09.713193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:09.869401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:10.025629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:10.244315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:10.404932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:10.544557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:10.696152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:10.847748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:10.999341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:11.157917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:11.305732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:11.462698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:11.620005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:11.761720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:11.917937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:12.058519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:12.214730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:12.361729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:12.510333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:12.661927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:12.807499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:12.948158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:13.097762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:13.243371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:13.384633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:13.517670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:13.673887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:13.815640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:13.956245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:14.112450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:14.253047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:14.397564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:14.549165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:14.699723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:14.850509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:14.992131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:15.139699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:15.292334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:15.547606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:15.700198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:15.849798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:15.993413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:16.144011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:16.290619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:16.430833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:16.579082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:16.705187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:16.845819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:16.986375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:17.126966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:17.267558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:17.413475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:17.558089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:17.712680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:17.860924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:17.996143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:18.139762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:18.288514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:18.431679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:18.581199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:18.745603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:18.915150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:19.081884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:19.263230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:19.424991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:19.596564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:19.743179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:19.900753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:20.071304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:20.244830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:20.401410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:20.564444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:20.741973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:20.893818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:21.034374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-22T18:25:32.618307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-22T18:25:32.861240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-22T18:25:33.079937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-22T18:25:33.330792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-22T18:25:33.606021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-22T18:25:21.741152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:23.370079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:24.602621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T18:25:25.078729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

MANDTBUKRSGJAHRHKONTKUNNRPRCTRKNA1_LAND1KNA1_ORT01ZTERMDUE_DATE_SOURCEVALUE_EURMWSTSMWST2BLARTT003T_LTEXTTBSLT_LTEXTMANSPCTLPCHISTORICRATINGCURRENTRATINGDATUMDUE_DATEGJAHR2DOCUMENT_DATEPAYMENT_DATEPAYMENT_TIMELINESSTIME_DELTA
03103201811187241NLKATWIJK63ZBD2T6070.00.0RVBilling doc.transferInvoiceNaNNaNNaNNaN2017-12-012017-12-2320192017-12-232018-01-091.022
13103201811195211NLWARNSVELD63ZBD2T10.00.0RVBilling doc.transferInvoiceNaNNaNNaNNaN2018-01-022018-01-1020162018-01-102018-01-090.08
2310320181154201NLOSS63ZBD2T140.00.0RVBilling doc.transferInvoiceNaNNaNNaNNaN2017-12-012017-12-2720192017-12-272018-01-091.026
33103201811187241NLKATWIJK63ZBD2T500.00.0RVBilling doc.transferInvoiceNaNNaNNaNNaN2018-01-022018-01-2720162018-01-272018-01-090.025
4310320181154201NLOSS63ZBD2T270.00.0RVBilling doc.transferInvoiceNaNNaNNaNNaN2018-01-022018-01-0720162018-01-072018-01-090.05
5310320181154201NLOSS63ZBD2T330.00.0RVBilling doc.transferInvoiceNaNNaNNaNNaN2018-01-022018-01-0720202018-01-072018-01-090.05
6310320181154201NLOSS63ZBD2T120.00.0RVBilling doc.transferInvoiceNaNNaNNaNNaN2018-01-022018-01-1120202018-01-112018-01-090.09
7310320181154201NLOSS63ZBD2T70.00.0RVBilling doc.transferInvoiceNaNNaNNaNNaN2018-01-022018-01-1120152018-01-112018-01-090.09
8310320181148181NLIJSSELSTEIN UT77ZBD2T10.00.0RVBilling doc.transferInvoiceNaNNaNNaNNaN2017-12-012017-12-1720202017-12-172018-01-101.016
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Last rows

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Duplicate rows

Most frequent

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